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% 本章节主要记录文章的摘要和翻译
% 摘要的引用用 brief 环境
\section{文章的摘要及其翻译}
\begin{brief}
Three-dimensional (3D) structure determination by single-particle analysis
of cryo-electron microscopy (cryo-EM) images requires many parameters to
be determined from extremely noisy data. This makes the method prone to
overfitting, that is, when structures describe noise rather than signal, in
particular near their resolution limit where noise levels are highest. 
Cryo-EM structures are typically filtered using \textit{ad hoc} procedures to prevent
overfitting, but the tuning of arbitrary parameters may lead to subjectivity
in the results. I describe a Bayesian interpretation of cryo-EM structure
determination, where smoothness in the reconstructed density is imposed
through a Gaussian prior in the Fourier domain. The statistical framework
dictates how data and prior knowledge should be combined, so that the
optimal 3D linear filter is obtained without the need for arbitrariness and
objective resolution estimates may be obtained. Application to experimental
data indicates that the statistical approach yields more reliable structures
than existing methods and is capable of detecting smaller classes in data sets
that contain multiple different structures.
\end{brief}
使用单颗粒技术解析冷冻电镜的图片得到三维结构, 需要从有很多噪音的数据中提取许多参数. 这使得这种方法容易过拟合, 即三维结构描述的不是信号而是噪音. 特别是在接近分辨率极限的时候, 噪音的水平最高. 过滤出来 cryo-EM 的结构使用的是一种专门的方法, 但是这种方法需要任意的调整参数, 这或许会导致结果的主观性. 作者描述了一中贝叶斯推断的方法, 来解析 cryo-EM 的结果. 在重建密度图时, 该方法通过高斯先验将 smoothness 暴露在傅里叶空间中. 统计框架可以展示出如何组合数据和先验信息以此来得到最优的 3D 滤波器, 并且这一过程没有任意性. 由此可以得到一个客观的分辨率估计. 应用到实验数据中表明: 统计方法比之前存在的方法生成的结构更可靠, 而且还可以探测到数据集中的亚类, 这些亚类包含不同的结构. 
